This solution addresses the massive waste problem in flower farming caused by demand uncertainty. By combining historical sales data with local event information, it predicts demand and aggregates supply from multiple farms to connect directly with florists and corporate clients. This improves farm profitability and reduces environmental impact from waste.
Why This Idea
The floral industry exhibits extreme seasonality, peaking during specific events like graduations. Small farms rely on guesswork for cultivation volumes due to a lack of accurate demand prediction, resulting in unsold flowers being discarded, causing massive financial losses and environmental pollution. The democratization of data analysis tools now enables small farms to make data-driven decisions. Additionally, the growing emphasis on ESG management among corporations has spiked demand for eco-friendly and community-supportive procurement methods, making it the perfect time to enter the B2B direct trade market. Collaboration is needed between a ‘Backend Engineer’ for building data pipelines and demand modeling, a ‘Frontend Engineer’ for developing intuitive dashboards for farms and florists, and a ‘Service Planner/PM’ to lead farm interviews and MVP validation.
Why This Problem Must Be Solved
About 70% of South Korean flower farms are small-scale, relying entirely on fluctuating wholesale auction prices. Without demand forecasting, 20-30% of produced flowers are discarded without securing fair prices. This not only erodes farm profits but causes soil and water pollution from mass disposal of plants treated with fertilizers and pesticides. Existing agricultural data services focus on food crops, failing to capture floral demand patterns. Farms still rely on past experience to decide planting schedules. Solving this structural contradiction, where farms bear disposal costs, is essential for industry sustainability.
Why Now Is the Right Time
As ESG management becomes mandatory in B2B procurement, more companies are willing to pay a premium for transparent supply chains that reduce waste and support local farms. Global VCs like a16z are heavily investing in B2B solutions solving traditional industry inefficiencies with data. The cost of data infrastructure has dropped, and open APIs make gathering external data (weather, trends, events) easy. The floral industry is a ‘blue ocean’ with slow digital transformation. Moving now establishes trust and high barriers to entry.
The Change This Creates
This system creates two major changes. First, it analyzes event schedules, past prices, and trends to provide farms with simple reports on ‘what, when, and how much to plant.’ Second, it aggregates expected harvest volumes from multiple small farms to connect directly with large florists or corporate clients. Users check planting schedules on a mobile app and secure buyers before harvest. Buyers get fresh flowers at stable prices without middlemen. Ultimately, it aims to reduce wasted flowers to near-zero and become the new distribution standard.
Why This Approach Works
Past floral distribution innovations mostly focused on B2C subscription deliveries. This solution intervenes at the top of the supply chain—the farm’s production planning—to solve fundamental supply-demand imbalances. It creates strong data network effects: more farm data improves matching accuracy and logistics. By facilitating forward contracts based on predicted data, it provides price stability for farms and supply certainty for buyers, creating a powerful lock-in effect.
How Far This Can Go
Initially, the MVP will target specific flowers (roses, chrysanthemums) in major floral complexes near Seoul. It will then expand to potted plants and nationwide coverage. Beyond Korea, the solution can be exported to global markets with high floral consumption but high wholesale reliance, like Japan. Long-term, accumulated data can be sold to seed developers or smart farm equipment companies, evolving into an ESG tech company entering the carbon credit market by measuring carbon reduction contributions.
Service Flow
graph LR
A[외부 데이터 수집] --> B[수요 예측 및 재배 추천]
B --> C[농가 재배 계획 등록]
C --> D[공동 물량 취합]
D --> E[기업/화원 선도 거래]
E --> F[안정적 납품 및 폐기 감소]
Business Model
graph TD
A[기업/화원 고객] -->|선도 거래 대금| B[통합 플랫폼]
B -->|수수료 공제 후 대금| C[영세 화훼 농가]
C -->|재배 및 수확 데이터| B
B -->|수요 예측 리포트| C
Tags: 농업 혁신, 공급망 관리, ESG, 수급 불균형 해결